SemEval 2015 Task 10

task ID: semeval2015task10

Related Papers

CIS-positive: A Combination of Convolutional Neural Networks and Support Vector Machines for Sentiment Analysis in Twitter

Authors: nan

Publication: TIPSTER (2015)

DOI: nan

This paper describes our automatic sentiment analysis system – CIS-positive – for SemEval 2015 Task 10 “Sentiment Analysis in Twit- ter”, subtask B “Message Polarity Classifica- tion”. In this system, …...

22678

Gradiant-Analytics: Training Polarity Shifters with CRFs for Message Level Polarity Detection

Authors: nan

Publication: TIPSTER (2015)

DOI: nan

In this paper we present our solution for ob- taining sentiment at message-level of short sentences. The system combines the use of polarity dictionaries and Conditional Random Fields to obtain …...

22676

IOA: Improving SVM Based Sentiment Classification Through Post Processing

Authors: nan

Publication: TIPSTER (2015)

DOI: nan

This paper describes our systems for expression-level and message-level sentiment analysis – two subtasks of SemEval-2015 Task 10 on sentiment analysis in Twitter. First we built two baseline systems for …...

22675

TwitterHawk: A Feature Bucket Based Approach to Sentiment Analysis

Authors: nan

Publication: TIPSTER (2015)

DOI: nan

This paper describes TwitterHawk, a system for sentiment analysis of tweets which partici- pated in the SemEval-2015 Task 10, Subtasks A through D. The system performed com- petitively, most notably …...

22641